Preprints
https://doi.org/10.5194/gi-2021-32
https://doi.org/10.5194/gi-2021-32
 
11 Jan 2022
11 Jan 2022
Status: a revised version of this preprint was accepted for the journal GI and is expected to appear here in due course.

GeoAI: a review of Artificial Intelligence approaches for the interpretation of complex Geomatics data

Roberto Pierdicca1 and Marina Paolanti2,3 Roberto Pierdicca and Marina Paolanti
  • 1Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, Ancona, Italy
  • 2Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
  • 3Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy

Abstract. Researchers have explored the benefits and applications of modern artificial intelligence (AI) algorithms in different scenario. For the processing of geomatics data, AI offers overwhelming opportunities. Fundamental questions include how AI can be specifically applied to or must be specifically created for geomatics data. This change is also having a significant impact on geospatial data. The integration of AI approaches in geomatics has developed into the concept of Geospatial Artificial Intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. However, little systematic work currently exists on how researchers have applied AI for geospatial domains. Hence, this contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. Our analysis has covered several gaps, for instance defining relationships between AI-based approaches and geomatics data. First, technologies and tools used for data acquisition are outlined, with a particular focus on RGB images, thermal images, 3D point clouds, trajectories, and hyperspectral/multispectral images. Then, how AI approaches have been exploited for the interpretation of geomatic data is explained. Finally, a broad set of examples of applications are given, together with the specific method applied. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.

Roberto Pierdicca and Marina Paolanti

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2021-32', Anonymous Referee #1, 28 Feb 2022
    • AC1: 'Reply on RC1', Roberto Pierdicca, 22 Apr 2022
  • RC2: 'Comment on gi-2021-32', Anonymous Referee #2, 26 Mar 2022
    • AC2: 'Reply on RC2', Roberto Pierdicca, 22 Apr 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2021-32', Anonymous Referee #1, 28 Feb 2022
    • AC1: 'Reply on RC1', Roberto Pierdicca, 22 Apr 2022
  • RC2: 'Comment on gi-2021-32', Anonymous Referee #2, 26 Mar 2022
    • AC2: 'Reply on RC2', Roberto Pierdicca, 22 Apr 2022

Roberto Pierdicca and Marina Paolanti

Roberto Pierdicca and Marina Paolanti

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Short summary
This review paper attempts to discuss how, the disruption of AI based data processing, has changed significantly the way Geomatics data are processed and interpreted. Among the sub-disciplines that are included in the umbrella of Geomatics, we focused on the kind of data that have been more recently faced by the research community, and tried to outline the recent trends and future research directions.